Sang Woong Lee

Rapid and Brief communication: Low resolution face recognition based on support vector data description

In the face recognition process, it is important to deal with a facial image of low-resolution. For low-resolution face recognition, we propose a new method of extending the SVDD, which is one of the most well-known support vector learning methods for the one-class problem. The proposed method can recognize a person even with a low-resolution image.

Volume Motion Template for View-Invariant Gesture Recognition

The representation of gestures changes dynamically, depending on camera viewpoints. This camera viewpoints problem is difficult to solve in environments with a single directional camera, since the shape and motion information for representing gestures is different at different viewpoints. In view-based methods, data for each viewpoint is required, which is ineffective and ambiguous in recognizing gestures. In this paper, we propose a volume motion template (VMT) to overcome the viewpoint problem in a single-directional stereo camera environment. The VMT represents motion information in 3D space using disparity maps. Motion orientation is determined with 3D motion information. The projection of VMT at the optimal virtual viewpoint can be obtained by motion orientation. The proposed method is not only independent of variations of viewpoints, but also can represent depth motion. The proposed method has been evaluated in view-invariant representation and recognition using the gesture sequences which include parallel motion in an optical axis. The experimental results demonstrated the effectiveness of the proposed VMT for view-invariant gesture recognition

A WALKING GUIDANCE SYSTEM FOR THE VISUALLY IMPAIRED

In this paper, we present a walking guidance system for the visually impaired pedestrians. The system has been designed to help the visually impaired by responding intelligently to various situations that can occur in unrestricted natural outdoor environments when walking and finding the destinations. It involves the main functions of people detection, text recognition, face recognition. In addition, added sophisticated functions of walking path guidance using Differential Global Positioning System, obstacle detection using a stereo camera and voice user, interface are included. In order to operate all functions concurrently, we develop approaches in real situations and integrate them. Finally, we experiment on a prototype system under natural environments in order to verify our approaches. The results show that our approaches are applicable to real situations.

Face recognition under arbitrary illumination using illuminated exemplars

Recently, the importance of face recognition has been increasingly emphasized since popular CCD cameras are distributed to various applications. However, facial images are dramatically changed by lighting variations, so that facial appearance changes caused serious performance degradation in face recognition. Many researchers have tried to overcome these illumination problems using diverse approaches, which have required a multiple registered images per person or the prior knowledge of lighting conditions. In this paper, we propose a new method for face recognition under arbitrary lighting conditions, given only a single registered image and training data under unknown illuminations. Our proposed method is based on the illuminated exemplars which are synthesized from photometric stereo images of training data. The linear combination of illuminated exemplars can represent the new face and the weighted coefficients of those illuminated exemplars are used as identity signature. We make experiments for verifying our approach and compare it with two traditional approaches. As a result, higher recognition rates are reported in these experiments using the illumination subset of Max-Planck Institute face database and Korean face database.

Real-Time gesture recognition using 3d motion history model

In this paper, we present a novel method for real time gesture recognition with 3D Motion History Model (MHM). There are two difficult problems in gesture recognition: the camera view and the duration of gesture. First, we solved the camera view problem which is very difficult in the environment of single directional camera (e.g., monocular or stereo camera). Utilizing 3D-MHM with the disparity information, not only this problem is solved but also the reliability of recognition and the scalability of system are improved. Second, we proposed the dynamic history buffering (DHB) to solve the duration problem that comes from the variation of gesture velocity at every performing time. DHB improves the problem using magnitude of motion. We implemented a real-time system and performed gesture recognition experiments. The system using 3D-MHM achieves better results of recognition than using only 2D motion information.

MULTIPLE HUMAN DETECTION AND TRACKING BASED ON WEIGHTED TEMPORAL TEXTURE FEATURES

In this paper, we present a method of tracking and identifying persons in video images taken by a fixed camera situated at an entrance. In video sequences a person may be totally or partially occlu...

Facial image reconstruction by SVDD-Based pattern de-noising

The SVDD (support vector data description) is one of the most well-known one-class support vector learning methods, in which one tries the strategy of utilizing balls defined on the feature space in order to distinguish a set of normal data from all other possible abnormal objects. In this paper, we consider the problem of reconstructing facial images from the partially damaged ones, and propose to use the SVDD-based de-noising for the reconstruction. In the proposed method, we deal with the shape and texture information separately. We first solve the SVDD problem for the data belonging to the given prototype facial images, and model the data region for the normal faces as the ball resulting from the SVDD problem. Next, for each damaged input facial image, we project its feature vector onto the decision boundary of the SVDD ball so that it can be tailored enough to belong to the normal region. Finally, we obtain the image of the reconstructed face by obtaining the pre-image of the projection, and then further processing with its shape and texture information. The applicability of the proposed method is illustrated via some experiments dealing with damaged facial images.

Face Reconstruction with Low Resolution Facial Images by Feature Vector Projection in Kernel Space

In spite of increasing interest in person identification based on biometrics, face recognition technology has not been applied into real world. It is caused by appearance changes such as illumination, noise, degradation, and occlusion. Among these problems, we focus on the low resolution problem and propose a new face recognition method of extending the SVDD (support vector data description). In the proposed method, we first solve the SVDD problem for the data belonging to the given prototype facial images, and model the data region for the normal faces as the ball resulting from the SVDD problem. Next, for each input facial image in low resolution, we project its feature vector onto the decision boundary of the SVDD ball so that it can be tailored enough to belong to the normal region. Finally, we synthesize facial images which are obtained from the pre-image of the projection, and then perform the face recognition. The applicability of the proposed method is illustrated via some experiments using general recognition algorithm

Authenticating corrupted photo images based on noise parameter estimation

Photo image authentication is an interesting and demanding field in the computer vision and image processing community. This research is motivated by its wide range of applications, which include smart card authentication systems, biometric passport systems, etc. In this paper, we propose a method of authenticating corrupted photo images based on noise parameter estimation. The proposed method first generates corrupted images by adjusting the noise parameters in the initial training phase. This set of corrupted images and the noise parameters can be represented by a linear combination of the prototypes of the corrupted images and the noise parameters. In the testing phase, the noise parameters of the corrupted photo image can be estimated with a corrupted image and an original image. Finally, we can make a synthesized photo image from the original photo image using the estimated noise parameters and verify it with the corrupted photo image. The experimental results show that the proposed method can estimate the noise parameters accurately and improve the performance of photo image authentication.

SVDD-Based Illumination Compensation for Face Recognition

Illumination change is one of most important and difficult problems which prevent from applying face recognition to real applications. For solving this, we propose a method to compensate for different illumination conditions based on SVDD(Support Vector Data Description). In the proposed method, we first consider the SVDD training for the data belonging to the facial images under various illuminations, and model the data region for each illumination as the ball resulting from the SVDD training. Next, we compensate for illumination changes using feature vector projection onto the decision boundary of the SVDD ball. Finally, we obtain the pre-image under the identical illumination with input image. By repeated for each person, we can recognize a person with facial images under same illumination. We also perform the face recognition in order to verify the efficacy of proposed method.